Research2d ago
Apple Unlocks Parallel Training For Large RNNs
ParaRNN enables the parallel training of nonlinear recurrent neural networks, overcoming the sequential bottlenecks that previously limited their scale. This architecture allows RNNs to reach billions of parameters while maintaining efficient inference. Apple researchers now offer a viable alternative to attention-based models for developers targeting resource-constrained hardware and low-memory deployments.